Aggregation-based Multilevel Methods for Lattice QCD

In Lattice QCD computations a substantial amount of work is spent in solving the Dirac equation. In the recent past it has been observed that conventional Krylov solvers tend to critically slow down for large lattices and small quark masses. We present a Schwarz alternating procedure (SAP) multilevel method as a solver for the Clover improved Wilson discretization of the Dirac equation. This approach combines two components (SAP and algebraic multigrid) that have separately been used in lattice QCD before. In combination with a bootstrap setup procedure we show that considerable speed-up over conventional Krylov subspace methods for realistic configurations can be achieved.Comment: Talk presented at the XXIX International Symposium on Lattice Field Theory, July 10-16, 2011, Lake Tahoe, Californi

Fluctuations and correlations in high temperature QCD

We calculate second- and fourth-order cumulants of conserved charges in a temperature range stretching from the QCD transition region towards the realm of (resummed) perturbation theory. We perform lattice simulations with staggered quarks; the continuum extrapolation is based on $N_t=10\dots24$ in the crossover-region and $N_t=8\dots16$ at higher temperatures. We find that the Hadron Resonance Gas model predictions describe the lattice data rather well in the confined phase. At high temperatures (above $\sim$250 MeV) we find agreement with the three-loop Hard Thermal Loop results.Comment: 18 pages revtex, 13 figure

Disconnected contributions to the spin of the nucleon

The spin decomposition of the proton is a long-standing topic of much interest in hadronic physics. Lattice QCD has had much success in calculating the connected contributions to the quark spin. However, complete calculations, which necessarily involve gluonic and strange-quark contributions, still present some challenges. These "disconnected" contributions typically involve small signals hidden against large statistical backgrounds and rely on computationally intensive stochastic techniques. In this work we demonstrate how a Feynman-Hellmann approach may be used to calculate such quantities, by measuring shifts in the proton energy arising from artificial modifications to the QCD action. We find a statistically significant non-zero result for the disconnected quark spin contribution to the proton of about -5% at a pion mass of 470 MeV

A review of High Performance Computing foundations for scientists

The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides new insights on systems which are analysed [3-6]. Knowing the fundamentals of computation can be very useful for scientists, for it can help them to improve the performance of their theoretical models and simulations. This review includes some technical essentials that can be useful to this end, and it is devised as a complement for researchers whose education is focused on scientific issues and not on technological respects. In this document we attempt to discuss the fundamentals of High Performance Computing (HPC) [7] in a way which is easy to understand without much previous background. We sketch the way standard computers and supercomputers work, as well as discuss distributed computing and discuss essential aspects to take into account when running scientific calculations in computers.Comment: 33 page

A note on entropic uncertainty relations of position and momentum

We consider two entropic uncertainty relations of position and momentum recently discussed in literature. By a suitable rescaling of one of them, we obtain a smooth interpolation of both for high-resolution and low-resolution measurements respectively. Because our interpolation has never been mentioned in literature before, we propose it as a candidate for an improved entropic uncertainty relation of position and momentum. Up to now, the author has neither been able to falsify nor prove the new inequality. In our opinion it is a challenge to do either one.Comment: 2 pages, 2 figures, 2 references adde

Quantitative Description of Pedestrian Dynamics with a Force based Model

This paper introduces a space-continuous force-based model for simulating pedestrian dynamics. The main interest of this work is the quantitative description of pedestrian movement through a bottleneck. Measurements of flow and density will be presented and compared with empirical data. The results of the proposed model show a good agreement with empirical data. Furthermore, we emphasize the importance of volume exclusion in force-based models.Comment: 4 pages, 7 figures, 2009 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT 2009), 15-18 September 2009, in Milano, Italy, 200

Charm quark effects on the strong coupling extracted from the static force

We compute the fermionic contribution to the strong coupling $\alpha_{qq}$ extracted from the static force in Lattice QCD up to order $g^4$ in perturbation theory. This allows us to subtract the leading fermionic lattice artifacts from recent determinations of $\alpha_{qq}$ produced in simulations of two dynamical charm quarks. Moreover, by using a suitable parametrization of the $\beta_{qq}$-function, we can evaluate the charm loop effects on $\alpha_{qq}$ in the continuum limit.Comment: 8 pages, 2 figures; Proceedings of the 35th International Symposium on Lattice Field Theory, Granada, Spai

Online Fault Classification in HPC Systems through Machine Learning

As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates. For this reason, detecting and classifying faults in HPC systems as they occur and initiating corrective actions before they can transform into failures will be essential for continued operation. In this paper, we propose a fault classification method for HPC systems based on machine learning that has been designed specifically to operate with live streamed data. We cast the problem and its solution within realistic operating constraints of online use. Our results show that almost perfect classification accuracy can be reached for different fault types with low computational overhead and minimal delay. We have based our study on a local dataset, which we make publicly available, that was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc